•This paper revisits the relationship between health and growthin light of modern endogenous growth theory. We propose a uni-fied framework that encompasses the growth effects of boththe rate of improvement of health and the level of health. Basedon cross-country regressions over the period 1960-2000, wefind that a higher initial level and a higher rate of improvementin life expectancy both have a significantly positive impact onper capita GDP growth. Then, restricting attention to OECD coun-tries, we find supporting evidence that only the reduction inmortality below age forty generates productivity gains, which inturn may explain why the positive correlation between healthand growth in cross-OECD country regressions appears to haveweakened since 1960.*Harvard University and Bruegel**Brown University†OECD, CREST(INSEE)

This research was commissioned by

Les Laboratoires Internationauxde Recherche

, which we gratefully acknowledge. We are mostgrateful to Brigitte Calle and Agnes Renard-Viard for their continuoussupport and encouragements, and to Daron Acemoglu, SimonJohnson, Peter Lorentzen and Romain Wacziarg for kindly sharingtheir data. This work benefited greatly from Aart Kraay’s numeroussuggestions. We also thank Gabrielle Fack for her help at an earlierstage of the project, and David Canning, Esther Duflo, MichaelKremer, Sophocles Mavroeidis, Chris Papageorgiou,Thomas Pikettyand David Weil for helpful comments. The findings, interpretations,and conclusions expressed in this paper are entirely those of theauthors and in particular they do not necessarily represent theviews of the OECD.

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1 Introduction

Can health explain cross-country di

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erences in levels and growth rates of income? This question is of primaryimportance, in particular in current debates on the costs and beneﬁts of new health programs. For example,public support for more universal health coverage is obviously a

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ected by whether or not people believethat improved health raises growth. While left-leaning politicians would still advocate such programs evenif they were not growth-enhancing, the programs would gain consensus if it could be shown, as it has beenfor education, that improving health is another way to increase a country’s growth potential.Basic economic intuition, supported by partial empirical evidence, suggests that health should somehowmatter for growth. First, individuals with higher life expectancy are likely to save more, and savings inturn feed back into capital accumulation and therefore into GDP growth as shown for instance by Zhang,Zhang and Lee (2003). Second, individuals with higher life expectancy are likely to invest more (or to havetheir parents invest more) in education, which in turn should be growth-enhancing

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. In an environmentmarked by low child mortality, parents are likely to choose a low level of fertility

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, which limits the growthof total population and supports per capita GDP growth. Finally, and more directly, healthier individualsare typically more productive, better at creating and adapting to new technologies and generally more ableto cope with the rapid changes characteristic of a high growth environment.

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A convenient way to address the relationship between health and growth is to look at health as a particularform of human capital (see Weil, 2007). Then, drawing on the parallel between health and education, onecan distinguish between two basic approaches. A ﬁrst approach, based on Mankiw-Romer-Weil (1992) andLucas (1988), would view health as a regular factor of production. Accordingly, output growth should becorrelated with the

rate of improvement

of health, in particular with the increase in life expectancy in acountry or region. A second approach, based on Nelson and Phelps (1966), would argue that a higher

stock

of health spurs growth by facilitating technological innovation and/or technological adoption. Accordingly,productivity growth should be positively correlated with the

level

of health, in particular with the initial orthe average level of life expectancy in a country or region over a given period.Both approaches have been followed by the existing macroeconomic literature on health and income/growth.Thus, Acemoglu and Johnson (2007), henceforth AJ, follow a Lucas approach and regress income growthon the increase in life expectancy between 1940 and 1980. To instrument for the growth of life expectancy,

See Lee(2003) and Galor (2005) for a discussion of the demographic transition. Using a large panel of countries spanningover the late XIXth and XXth centuries, Murtin (2009) displays empirical evidence that child mortality has been signiﬁcantlyand positively associated with fertility.

ects of health, especially early childhood health, on the pace of technological change.

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AJ exploit the wave of health innovations that occurred as of the 1950s and a

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ected all countries world-wide: more precisely, they use the pre-intervention distribution of mortality from 15 diseases and the dates of global interventions to construct a country-varying instrument for life expectancy. Then, when regressing percapita GDP growth on the growth of life expectancy over the 1940-1980 period, AJ ﬁnd that improvementsin life expectancy over that period have no signiﬁcant positive e

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ect. In contrast, Lorentzen, McMillan andWacziarg (2008), henceforth LMW, adopt a Nelson-Phelps approach and regress per capita GDP growthon average child and adult mortality rates over the period 1960-2000. LMW use seventeen instruments forthese two mortality indicators: a malaria ecology index - originally developed by Sachs et al. (2004) - whichcaptures the exogenous portion of malaria incidence, twelve climate variables, and four geographic featuresof countries, which are unlikely to be a

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ected by human activity and more particularly by income levels orgrowth rates. LMW then ﬁnd a strong e

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ect of mortality rates on income growth. In particular, they ﬁndthat adult mortality alone can account for all of Africa’s growth shortfall over the 1960-2000 period.

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In this paper we combine the two approaches and look at the joint e

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ect of health and health accumulationon economic growth, much in the spirit of Krueger and Lindahl (2001), who performed a similar exercisewhen looking at the e



ect of education on growth. We ﬁrst develop a uniﬁed framework for analyzing therelationship between health and growth, which embeds both level and accumulation e



ects. Then movingto the empirical part, we run cross-country growth regressions over the period 1960-2000 (the same periodas in Krueger-Lindahl) and show that over that period both the level and the accumulation of health havesigniﬁcant positive e



ects on growth of per capita GDP, even when we use the LMW instruments for thelevel and accumulation of life expectancy. We also show that these instruments pass such standard tests asthe Hansen test for joint exogeneity of instruments and the Stock-Yogo test for weak instruments. Our basicresults are further conﬁrmed by a robustness analysis which uses Bayesian techniques to assess the inﬂuenceof potential endogeneity biases on OLS estimates. Testing the inﬂuence of various priors on the probabilitydistribution of the correlation between residuals and explanatory variables, we ﬁnd a robust positive e

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ectof initial life expectancy and a somewhat less robust e

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ect of life expectancy growth. Together our resultsalso suggest that the omission of initial life expectancy, or the omission of the growth of life expectancy,from the RHS of our growth regressions may generate a downward bias on the estimated coe



cients.The key to reconciling our results with those of AJ is convergence in life expectancy. As we document

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In addition, LMW disentangle the negative e

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ects of mortality on investment and human capital accumulation from itspositive e

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ect on the fertility rate, and they ﬁnd that investment and fertility are the strongest channels underlying the positivee

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ect of health on growth.

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That the level of life expectancy should matter for growth is also consistent with recent work by Doppelhofer et al. (2004)and Cervellati and Sunde (2009). The latter show that the level of initial life expectancy is a strong predictor of the growth rate infertility. As a result, introducing initial life expectancy inside the regression helps control for the e

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ect of demographic transition.In particular the late decline in fertility should reduce population growth and thereby mitigate the negative Malthusian e

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ectof growth in life expectancy upon per capita GDP growth. The former use Bayesian averaging models techniques to show thatthe initial level of life expectancy is one of the most robust determinant of economic growth.